Detection of fish Object Detection Dataset and Pre-Trained Model by Project

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Explore Dataset

Here are a few use cases for this project:

Fishery Management and Species Conservation: The "Detection of Fish" model can be used by fishery managers and marine biologists to monitor and track the population of fish within natural habitats, allowing them to identify changes in the distribution of normal and abnormal fish, which can inform decisions on conservation measures or fishing restrictions.
Aquaculture Quality Control: Fish farm operators can use this computer vision model to assess the health of their fish stock, ensuring that only healthy fish are sold to consumers. By detecting abnormal fish, operators can quickly isolate and treat affected fish, reducing the spread of diseases or defects in the population.
Environmental Impact Assessments: Researchers can utilize the model to study the impact of environmental factors, such as pollution or waste, on fish populations in specific locations. By determining the ratio of normal to abnormal fish, they can assess the well-being of aquatic ecosystems and inform environmental protection policies.
Retail Seafood Inspection: The "Detection of Fish" model can be employed in seafood processing plants or markets to automatically inspect and grade fish based on their visual appearance. By identifying abnormal fish, retailers can ensure that they are providing high-quality seafood to their customers, and potentially minimizing the risk of foodborne illnesses.
Education and Citizen Science: The model can serve as an educational tool for students, researchers, and enthusiasts interested in learning about fish species, their classifications, and the factors affecting their well-being. By incorporating the "Detection of Fish" model into a user-friendly platform, citizen scientists can help contribute to ongoing research and monitoring efforts by documenting the various fish they encounter.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            detection-of-fish_dataset,
                            title = { Detection of fish Dataset },
                            type = { Open Source Dataset },
                            author = { Project },
                            howpublished = { \url{ https://universe.roboflow.com/project-28suc/detection-of-fish } },
                            url = { https://universe.roboflow.com/project-28suc/detection-of-fish },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-14 },
                            }